Key papers

• Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery (2025)
  Shuyi Jia et al.
  2.65
• Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation (2026)
  Paul Hagemann et al.
  1.94
• Ordered-to-disordered transfer learning with graph neural networks for formation-energy and HOMO-LUMO gap prediction in high-entropy perovskite oxides (2026)
  Panupol Untarabut et al.
  1.94
• MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science (2026)
  Zhan'ao Yao et al.
  1.89
• Mesh Graph Neural Network Framework for Accelerating Finite Element Simulation for Arbitrary Geometries (2026)
  Josiah D. Kunz et al.
  1.89
• Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys (2026)
  Takanori Kotama and Yang Huang
  1.83
• Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data (2026)
  Abril Azocar Guzman et al.
  1.78
• XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures (2026)
  Vitor F. Grizzi et al.
  1.78
• Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data (2026)
  Massimiliano Lupo Pasini et al.
  1.78
• Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation (2026)
  Urvi Awasthi et al.
  1.72
• A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks (2026)
  Brandon Schoener et al.
  1.67
• How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science (2026)
  Can Polat et al.
  1.67
• Stable Long-Horizon Spatiotemporal Prediction on Meshes Using Latent Multiscale Recurrent Graph Neural Networks (2026)
  Lionel Salesses et al.
  1.67
• OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs (2026)
  Mohammadmahdi Vahediahmar et al.
  1.67
• Higher-Order Knowledge Representations for Agentic Scientific Reasoning (2026)
  Isabella A. Stewart and Markus J. Buehler
  1.61
• MEIDNet: Multimodal generative AI framework for inverse materials design (2026)
  Anand Babu et al.
  1.61
• Representation of Inorganic Synthesis Reactions and Prediction: Graphical Framework and Datasets (2025)
  Samuel Andrello et al.
  1.56
• PolySet: Restoring the Statistical Ensemble Nature of Polymers for Machine Learning (2025)
  Khalid Ferji
  1.56
• Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery (2025)
  Hao Xu et al.
  1.50
• Benchmarking GNNs for OOD Materials Property Prediction with Uncertainty Quantification (2025)
  Liqin Tan et al.
  1.50
• Scaling Kinetic Monte-Carlo Simulations of Grain Growth with Combined Convolutional and Graph Neural Networks (2025)
  Zhihui Tian et al.
  1.50
• PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction (2025)
  \`Alex Sol\'e et al.
  1.50
• Transformers Discover Molecular Structure Without Graph Priors (2025)
  Tobias Kreiman et al.
  1.44
• SHA-256 Infused Embedding-Driven Generative Modeling of High-Energy Molecules in Low-Data Regimes (2025)
  Siddharth Verma et al.
  1.44
• Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability (2025)
  Rog\'erio Almeida Gouv\^ea et al.
  1.39
• Facet: highly efficient E(3)-equivariant networks for interatomic potentials (2025)
  Nicholas Miklaucic et al.
  1.39
• Unsupervised Atomic Data Mining via Multi-Kernel Graph Autoencoders for Machine Learning Force Fields (2025)
  Hong Sun and Joshua A. Vita and Amit Samanta and Vincenzo Lordi
  1.39
• Physics-Informed GCN-LSTM Framework for Long-Term Forecasting of 2D and 3D Microstructure Evolution (2025)
  Hamidreza Razavi et al.
  1.39
• FIRE-GNN: Force-informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties (2025)
  Circe Hsu et al.
  1.33
• XxaCT-NN: Structure Agnostic Multimodal Learning for Materials Science (2025)
  Jithendaraa Subramanian et al.
  1.28
• Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction (2025)
  Manasa Kaniselvan et al.
  1.28
• Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity (2025)
  Manuel Ricardo Guevara Garban et al.
  1.28
• Universal crystal material property prediction via multi-view geometric fusion in graph transformers (2025)
  Liang Zhang et al.
  1.28
• Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence (2025)
  Mads-Peter Verner Christiansen and Bj{\o}rk Hammer
  1.28
• Graph Learning Metallic Glass Discovery from Wikipedia (2025)
  K.-C. Ouyang et al.
  1.28
• Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data (2025)
  Massimiliano Lupo Pasini and Jong Youl Choi and Pei Zhang and Kshitij Mehta and Rylie Weaver and Ashwin M. Aji and Karl W. Schulz and Jorda Polo and Prasanna Balaprakash
  1.22
• SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction (2025)
  Junchi Liu et al.
  1.17
• Quotient Complex Transformer (QCformer) for Perovskite Data Analysis (2025)
  Xinyu You et al.
  1.17
• Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding (2025)
  Can Polat et al.
  1.17
• Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling (2025)
  Chaojian Li et al.
  1.11
• MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery (2025)
  Lingyu Kong et al.
  1.11
• Graph Neural Network Prediction of Nonlinear Optical Properties (2025)
  Yomn Alkabakibi et al.
  1.11
• CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling (2025)
  Yusei Ito et al.
  1.06
• A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery (2025)
  Yusuke Hashimoto et al.
  1.06
• Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution (2025)
  Moin Uddin Maruf and Sungmin Kim and Zeeshan Ahmad
  1.06
• Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites (2025)
  Marko Petkovi\'c et al.
  1.06
• CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction (2025)
  Jaewan Lee et al.
  1.00
• Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks (2025)
  Markus J. Buehler
  1.00
• Co-Evolution-Based Metal-Binding Residue Prediction with Graph Neural Networks (2025)
  Sayedmohammadreza Rastegari et al.
  1.00
• Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers (2025)
  Markus J. Buehler
  0.94
• DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules (2025)
  Hongwei Du et al.
  0.94
• Discovery of sustainable energy materials via the machine-learned material space (2025)
  Malte Grunert et al.
  0.94
• In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR (2025)
  Markus J. Buehler
  0.94
• A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation (2025)
  \`Alex Sol\'e et al.
  0.94
• E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products (2025)
  Yunyang Li et al.
  0.94
• Do Graph Neural Networks Work for High Entropy Alloys? (2024)
  Hengrui Zhang et al.
  0.78
• Orbital Graph Convolutional Neural Network for Material Property Prediction (2020)
  Mohammadreza Karamad et al.
  —
• Graph Neural Network for Metal Organic Framework Potential Energy Approximation (2020)
  Shehtab Zaman et al.
  —
• On the equivalence of molecular graph convolution and molecular wave function with poor basis set (2020)
  Masashi Tsubaki and Teruyasu Mizoguchi
  —
• Machine Learning for Phase Behavior in Active Matter Systems (2020)
  Austin R. Dulaney and John F. Brady
  —